Improving translation memory matching and retrieval using paraphrases

Abstract

Most current translation memory (TM) systems work on the string level (character or word level) and lack semantic knowledge while matching. They use simple edit-distance (ED) calculated on the surface form or some variation on it (stem, lemma), which does not take into consideration any semantic aspects in matching. This paper presents a novel and efficient approach to incorporating semantic information in the form of paraphrasing (PP) in the ED metric. The approach computes ED while efficiently considering paraphrases using dynamic programming and greedy approximation. In addition to using automatic evaluation metrics like BLEU and METEOR, we have carried out an extensive human evaluation in which we measured post-editing time, keystrokes, HTER, HMETEOR, and carried out three rounds of subjective evaluations. Our results show that PP substantially improves TM matching and retrieval, resulting in translation performance increases when translators use paraphrase-enhanced TMs.

Keywords

Notes

Acknowledgments

The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme FP7/2007–2013/ under REA Grant Agreement No. 317471 and the EC-funded project QT21 under Horizon 2020, ICT 17, Grant Agreement No. 645452.

Simard M, Fujita A (2012) A poor man’s translation memory using machine translation evaluation metrics. In: Proceedings of the tenth conference of the association for machine translation in the Americas. San Diego, CAGoogle Scholar